Abstract
Abstract. In the current multi-model ensemble approach climate model simulations are combined a posteriori. In the method of this study the models in the ensemble exchange information during simulations and learn from historical observations to combine their strengths into a best representation of the observed climate. The method is developed and tested in the context of small chaotic dynamical systems, like the Lorenz 63 system. Imperfect models are created by perturbing the standard parameter values. Three imperfect models are combined into one super-model, through the introduction of connections between the model equations. The connection coefficients are learned from data from the unperturbed model, that is regarded as the truth. The main result of this study is that after learning the super-model is a very good approximation to the truth, much better than each imperfect model separately. These illustrative examples suggest that the super-modeling approach is a promising strategy to improve weather and climate simulations.
Highlights
There is a broad scientific consensus that our climate is warming due to anthropogenic emissions of greenhouse gasses (IPCC, 2007)
Due to the large impacts of climate change on society there is a growing need to widely sample and assess the possible climate change related to the plausible scenarios for future emissions
For instance a temperature bias of several degrees in annual mean temperatures in large regions of the globe is not uncommon in the simulations of the present climate (IPCC, 2007). These models are used to simulate the response of the climate system to future emission scenarios of greenhouse gasses
Summary
There is a broad scientific consensus that our climate is warming due to anthropogenic emissions of greenhouse gasses (IPCC, 2007). The connection coefficients are learned from historical observations This way the super-model learns to combine the strengths of the individual models in order to optimally reproduce the historical climate. Van den Berge et al.: Combining imperfect models through learning coupling the momentum fluxes from one model and the heat and fresh water fluxes from the other to the ocean model. Another indication that this approach might be feasible is found in the practice of data assimilation (Compo et al, 2006).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
